Fast activation maximization for molecular sequence design.

ABSTRACT:

Background

Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence.

Results

Here, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in ear

SUBMITTER: Linder J 

PROVIDER: S-EPMC8527647 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature